Agent profiler to monitor activities and performance of software agents

ABSTRACT

In one embodiment, a software agent profiler process attaches to an application and a primary instrumentation interface for the application, and discovers one or more software agents associated with the application. The software agent profiler process may then launch the one or more software agents within an encapsulated container environment of the software agent profiler process by configuring each of the one or more software agents, respectively, to point to a proxy instrumentation interface of the software agent profiler process instead of the primary instrumentation interface for the application. As such, the software agent profiler process may receive calls from the one or more software agents on the proxy instrumentation interface of the software agent profiler process, and can manage the calls from the one or more application agents prior to the calls being passed to the primary instrumentation interface for the application.

RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.16/585,113, filed on Sep. 27, 2019, which claims priority to U.S.Provisional Application No. 62/741,890, filed Oct. 5, 2018, bothentitled AGENT PROFILER TO MONITOR ACTIVITIES AND PERFORMANCE OFSOFTWARE AGENTS, by Walter Theodore Hulick, Jr., the entire contents ofeach of which are incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates generally to computer systems, and, moreparticularly, to an agent profiler to monitor activities and performanceof software agents.

BACKGROUND

Due to the complexity of software systems, it is becoming increasinglydifficult to maintain the highest level of service performance and userexperience. Many software tools, including performance monitoringsystems, make use of software agents, such as Java Agents, to provideinstrumentation capabilities to an application.

However, there are currently no mechanisms to measure the resourceoverhead of software agents, or the actions of the agents, oridentifying potential conflicts between multiple agents.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to thefollowing description in conjunction with the accompanying drawings inwhich like reference numerals indicate identically or functionallysimilar elements, of which:

FIGS. 1A-1B illustrate an example computer network;

FIG. 2 illustrates an example computing device/node;

FIG. 3 illustrates an example application intelligence platform;

FIG. 4 illustrates an example system for implementing the exampleapplication intelligence platform;

FIG. 5 illustrates an example computing system implementing thedisclosed technology;

FIGS. 6A-6B illustrate an example of an agent profiler configuration tomonitor activities and performance of software agents in accordance withone or more embodiments described herein;

FIGS. 7A-7B illustrate an example of an agent profiler switch inaccordance with one or more embodiments described herein;

FIG. 8 illustrates an example of agent profiler statistics in accordancewith one or more embodiments described herein;

FIG. 9 illustrates an example table of agent threads and associatedinformation in accordance with one or more embodiments described herein;

FIG. 10 illustrates an example table of threads owning locks and threadswaiting on those locks in accordance with one or more embodimentsdescribed herein;

FIG. 11 illustrates an example table of all threads waiting onparticular locks in accordance with one or more embodiments describedherein;

FIG. 12 illustrates an example table of classes instrumented by managedagents in accordance with one or more embodiments described herein;

FIG. 13 illustrates an example table showing retransformed classes inaccordance with one or more embodiments described herein;

FIG. 14 illustrates an example table of sampled stack statistics from astack sampler in accordance with one or more embodiments describedherein;

FIG. 15 illustrates an example table showing sampler “hotspots” inaccordance with one or more embodiments described herein; and

FIG. 16 illustrates an example simplified procedure for agent profilerto monitor activities and performance of software agents in accordancewith one or more embodiments described herein.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

According to one or more embodiments of the disclosure, a software agentprofiler process attaches to an application and a primaryinstrumentation interface for the application, and discovers one or moresoftware agents associated with the application. The software agentprofiler process may then launch the one or more software agents withinan encapsulated container environment of the software agent profilerprocess by configuring each of the one or more software agents,respectively, to point to a proxy instrumentation interface of thesoftware agent profiler process instead of the primary instrumentationinterface for the application. As such, the software agent profilerprocess may receive calls from the one or more software agents on theproxy instrumentation interface of the software agent profiler process,and can manage the calls from the one or more application agents priorto the calls being passed to the primary instrumentation interface forthe application.

Other embodiments are described below, and this overview is not meant tolimit the scope of the present disclosure.

Description

A computer network is a geographically distributed collection of nodesinterconnected by communication links and segments for transporting databetween end nodes, such as personal computers and workstations, or otherdevices, such as sensors, etc. Many types of networks are available,ranging from local area networks (LANs) to wide area networks (WANs).LANs typically connect the nodes over dedicated private communicationslinks located in the same general physical location, such as a buildingor campus. WANs, on the other hand, typically connect geographicallydispersed nodes over long-distance communications links, such as commoncarrier telephone lines, optical lightpaths, synchronous opticalnetworks (SONET), synchronous digital hierarchy (SDH) links, orPowerline Communications (PLC), and others. The Internet is an exampleof a WAN that connects disparate networks throughout the world,providing global communication between nodes on various networks. Othertypes of networks, such as field area networks (FANs), neighborhood areanetworks (NANs), personal area networks (PANs), enterprise networks,etc. may also make up the components of any given computer network.

The nodes typically communicate over the network by exchanging discreteframes or packets of data according to predefined protocols, such as theTransmission Control Protocol/Internet Protocol (TCP/IP). In thiscontext, a protocol consists of a set of rules defining how the nodesinteract with each other. Computer networks may be furtherinterconnected by an intermediate network node, such as a router, toextend the effective “size” of each network.

Smart object networks, such as sensor networks, in particular, are aspecific type of network having spatially distributed autonomous devicessuch as sensors, actuators, etc., that cooperatively monitor physical orenvironmental conditions at different locations, such as, e.g.,energy/power consumption, resource consumption (e.g., water/gas/etc. foradvanced metering infrastructure or “AMI” applications) temperature,pressure, vibration, sound, radiation, motion, pollutants, etc. Othertypes of smart objects include actuators, e.g., responsible for turningon/off an engine or perform any other actions. Sensor networks, a typeof smart object network, are typically shared-media networks, such aswireless or power-line communication networks. That is, in addition toone or more sensors, each sensor device (node) in a sensor network maygenerally be equipped with a radio transceiver or other communicationport, a microcontroller, and an energy source, such as a battery.Generally, size and cost constraints on smart object nodes (e.g.,sensors) result in corresponding constraints on resources such asenergy, memory, computational speed and bandwidth.

FIG. 1A is a schematic block diagram of an example computer network 100illustratively comprising nodes/devices, such as a plurality ofrouters/devices interconnected by links or networks, as shown. Forexample, customer edge (CE) routers 110 may be interconnected withprovider edge (PE) routers 120 (e.g., PE-1, PE-2, and PE-3) in order tocommunicate across a core network, such as an illustrative networkbackbone 130. For example, routers 110, 120 may be interconnected by thepublic Internet, a multiprotocol label switching (MPLS) virtual privatenetwork (VPN), or the like. Data packets 140 (e.g., traffic/messages)may be exchanged among the nodes/devices of the computer network 100over links using predefined network communication protocols such as theTransmission Control Protocol/Internet Protocol (TCP/IP), User DatagramProtocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relayprotocol, or any other suitable protocol. Those skilled in the art willunderstand that any number of nodes, devices, links, etc. may be used inthe computer network, and that the view shown herein is for simplicity.

In some implementations, a router or a set of routers may be connectedto a private network (e.g., dedicated leased lines, an optical network,etc.) or a virtual private network (VPN), such as an MPLS VPN thanks toa carrier network, via one or more links exhibiting very differentnetwork and service level agreement characteristics.

FIG. 1B illustrates an example of network 100 in greater detail,according to various embodiments. As shown, network backbone 130 mayprovide connectivity between devices located in different geographicalareas and/or different types of local networks. For example, network 100may comprise local/branch networks 160, 162 that include devices/nodes10-16 and devices/nodes 18-20, respectively, as well as a datacenter/cloud environment 150 that includes servers 152-154. Notably,local networks 160-162 and data center/cloud environment 150 may belocated in different geographic locations. Servers 152-154 may include,in various embodiments, any number of suitable servers or othercloud-based resources. As would be appreciated, network 100 may includeany number of local networks, data centers, cloud environments,devices/nodes, servers, etc.

In some embodiments, the techniques herein may be applied to othernetwork topologies and configurations. For example, the techniquesherein may be applied to peering points with high-speed links, datacenters, etc. Furthermore, in various embodiments, network 100 mayinclude one or more mesh networks, such as an Internet of Thingsnetwork. Loosely, the term “Internet of Things” or “IoT” refers touniquely identifiable objects (things) and their virtual representationsin a network-based architecture. In particular, the next frontier in theevolution of the Internet is the ability to connect more than justcomputers and communications devices, but rather the ability to connect“objects” in general, such as lights, appliances, vehicles, heating,ventilating, and air-conditioning (HVAC), windows and window shades andblinds, doors, locks, etc. The “Internet of Things” thus generallyrefers to the interconnection of objects (e.g., smart objects), such assensors and actuators, over a computer network (e.g., via IP), which maybe the public Internet or a private network.

Notably, shared-media mesh networks, such as wireless networks, areoften on what is referred to as Low-Power and Lossy Networks (LLNs),which are a class of network in which both the routers and theirinterconnect are constrained: LLN routers typically operate withconstraints, e.g., processing power, memory, and/or energy (battery),and their interconnects are characterized by, illustratively, high lossrates, low data rates, and/or instability. LLNs are comprised ofanything from a few dozen to thousands or even millions of LLN routers,and support point-to-point traffic (between devices inside the LLN),point-to-multipoint traffic (from a central control point such at theroot node to a subset of devices inside the LLN), andmultipoint-to-point traffic (from devices inside the LLN towards acentral control point). Often, an IoT network is implemented with anLLN-like architecture. For example, as shown, local network 160 may bean LLN in which CE-2 operates as a root node for nodes/devices 10-16 inthe local mesh, in some embodiments.

FIG. 2 is a schematic block diagram of an example computing device(e.g., apparatus) 200 that may be used with one or more embodimentsdescribed herein, e.g., as any of the devices shown in FIGS. 1A-1Babove, and particularly as specific devices as described further below.The device may comprise one or more network interfaces 210 (e.g., wired,wireless, etc.), at least one processor 220, and a memory 240interconnected by a system bus 250, as well as a power supply 260 (e.g.,battery, plug-in, etc.).

The network interface(s) 210 contain the mechanical, electrical, andsignaling circuitry for communicating data over links coupled to thenetwork 100, e.g., providing a data connection between device 200 andthe data network, such as the Internet. The network interfaces may beconfigured to transmit and/or receive data using a variety of differentcommunication protocols. For example, interfaces 210 may include wiredtransceivers, wireless transceivers, cellular transceivers, or the like,each to allow device 200 to communicate information to and from a remotecomputing device or server over an appropriate network. The same networkinterfaces 210 also allow communities of multiple devices 200 tointerconnect among themselves, either peer-to-peer, or up and down ahierarchy. Note, further, that the nodes may have two different types ofnetwork connections 210, e.g., wireless and wired/physical connections,and that the view herein is merely for illustration. Also, while thenetwork interface 210 is shown separately from power supply 260, fordevices using powerline communication (PLC) or Power over Ethernet(PoE), the network interface 210 may communicate through the powersupply 260, or may be an integral component of the power supply.

The memory 240 comprises a plurality of storage locations that areaddressable by the processor 220 and the network interfaces 210 forstoring software programs and data structures associated with theembodiments described herein. The processor 220 may comprise hardwareelements or hardware logic adapted to execute the software programs andmanipulate the data structures 245. An operating system 242, portions ofwhich are typically resident in memory 240 and executed by theprocessor, functionally organizes the device by, among other things,invoking operations in support of software processes and/or servicesexecuting on the device. These software processes and/or services maycomprise one or more functional processes 246, and on certain devices,an illustrative “web application security communication” process 248, asdescribed herein. Notably, functional processes 246, when executed byprocessor(s) 220, cause each particular device 200 to perform thevarious functions corresponding to the particular device's purpose andgeneral configuration. For example, a router would be configured tooperate as a router, a server would be configured to operate as aserver, an access point (or gateway) would be configured to operate asan access point (or gateway), a client device would be configured tooperate as a client device, and so on.

It will be apparent to those skilled in the art that other processor andmemory types, including various computer-readable media, may be used tostore and execute program instructions pertaining to the techniquesdescribed herein. Also, while the description illustrates variousprocesses, it is expressly contemplated that various processes may beembodied as modules configured to operate in accordance with thetechniques herein (e.g., according to the functionality of a similarprocess). Further, while the processes have been shown separately, thoseskilled in the art will appreciate that processes may be routines ormodules within other processes.

Application Intelligence Platform

The embodiments herein relate to an application intelligence platformfor application performance management. In one aspect, as discussed withrespect to FIGS. 3-5 below, performance within a networking environmentmay be monitored, specifically by monitoring applications and entities(e.g., transactions, tiers, nodes, and machines) in the networkingenvironment using agents installed at individual machines at theentities. As an example, applications may be configured to run on one ormore machines (e.g., a customer will typically run one or more nodes ona machine, where an application consists of one or more tiers, and atier consists of one or more nodes). The agents collect data associatedwith the applications of interest and associated nodes and machineswhere the applications are being operated. Examples of the collecteddata may include performance data (e.g., metrics, metadata, etc.) andtopology data (e.g., indicating relationship information). Theagent-collected data may then be provided to one or more servers orcontrollers to analyze the data.

FIG. 3 is a block diagram of an example application intelligenceplatform 300 that can implement one or more aspects of the techniquesherein. The application intelligence platform is a system that monitorsand collects metrics of performance data for an application environmentbeing monitored. At the simplest structure, the application intelligenceplatform includes one or more agents 310 and one or moreservers/controllers 320. Note that while FIG. 3 shows four agents (e.g.,Agent 1 through Agent 4) communicatively linked to a single controller,the total number of agents and controllers can vary based on a number offactors including the number of applications monitored, how distributedthe application environment is, the level of monitoring desired, thelevel of user experience desired, and so on.

The controller 320 is the central processing and administration serverfor the application intelligence platform. The controller 320 serves abrowser-based user interface (UI) 330 that is the primary interface formonitoring, analyzing, and troubleshooting the monitored environment.The controller 320 can control and manage monitoring of businesstransactions (described below) distributed over application servers.Specifically, the controller 320 can receive runtime data from agents310 (and/or other coordinator devices), associate portions of businesstransaction data, communicate with agents to configure collection ofruntime data, and provide performance data and reporting through theinterface 330. The interface 330 may be viewed as a web-based interfaceviewable by a client device 340. In some implementations, a clientdevice 340 can directly communicate with controller 320 to view aninterface for monitoring data. The controller 320 can include avisualization system 350 for displaying the reports and dashboardsrelated to the disclosed technology. In some implementations, thevisualization system 350 can be implemented in a separate machine (e.g.,a server) different from the one hosting the controller 320.

Notably, in an illustrative Software as a Service (SaaS) implementation,a controller instance 320 may be hosted remotely by a provider of theapplication intelligence platform 300. In an illustrative on-premises(On-Prem) implementation, a controller instance 320 may be installedlocally and self-administered.

The controllers 320 receive data from different agents 310 (e.g., Agents1-4) deployed to monitor applications, databases and database servers,servers, and end user clients for the monitored environment. Any of theagents 310 can be implemented as different types of agents with specificmonitoring duties. For example, application agents may be installed oneach server that hosts applications to be monitored. Instrumenting anagent adds an application agent into the runtime process of theapplication.

Database agents, for example, may be software (e.g., a Java program)installed on a machine that has network access to the monitoreddatabases and the controller. Database agents query the monitoreddatabases in order to collect metrics and pass those metrics along fordisplay in a metric browser (e.g., for database monitoring and analysiswithin databases pages of the controller's UI 330). Multiple databaseagents can report to the same controller. Additional database agents canbe implemented as backup database agents to take over for the primarydatabase agents during a failure or planned machine downtime. Theadditional database agents can run on the same machine as the primaryagents or on different machines. A database agent can be deployed ineach distinct network of the monitored environment. Multiple databaseagents can run under different user accounts on the same machine.

Standalone machine agents, on the other hand, may be standalone programs(e.g., standalone Java programs) that collect hardware-relatedperformance statistics from the servers (or other suitable devices) inthe monitored environment. The standalone machine agents can be deployedon machines that host application servers, database servers, messagingservers, Web servers, etc. A standalone machine agent has an extensiblearchitecture (e.g., designed to accommodate changes).

End user monitoring (EUM) may be performed using browser agents andmobile agents to provide performance information from the point of viewof the client, such as a web browser or a mobile native application.Through EUM, web use, mobile use, or combinations thereof (e.g., by realusers or synthetic agents) can be monitored based on the monitoringneeds. Notably, browser agents (e.g., agents 310) can include Reportersthat report monitored data to the controller.

Monitoring through browser agents and mobile agents are generally unlikemonitoring through application agents, database agents, and standalonemachine agents that are on the server. In particular, browser agents maygenerally be embodied as small files using web-based technologies, suchas JavaScript agents injected into each instrumented web page (e.g., asclose to the top as possible) as the web page is served, and areconfigured to collect data. Once the web page has completed loading, thecollected data may be bundled into a beacon and sent to an EUMprocess/cloud for processing and made ready for retrieval by thecontroller. Browser real user monitoring (Browser RUM) provides insightsinto the performance of a web application from the point of view of areal or synthetic end user. For example, Browser RUM can determine howspecific Ajax or iframe calls are slowing down page load time and howserver performance impact end user experience in aggregate or inindividual cases.

A mobile agent, on the other hand, may be a small piece of highlyperformant code that gets added to the source of the mobile application.Mobile RUM provides information on the native mobile application (e.g.,iOS or Android applications) as the end users actually use the mobileapplication. Mobile RUM provides visibility into the functioning of themobile application itself and the mobile application's interaction withthe network used and any server-side applications with which the mobileapplication communicates.

Application Intelligence Monitoring: The disclosed technology canprovide application intelligence data by monitoring an applicationenvironment that includes various services such as web applicationsserved from an application server (e.g., Java virtual machine (JVM),Internet Information Services (IIS), Hypertext Preprocessor (PHP) Webserver, etc.), databases or other data stores, and remote services suchas message queues and caches. The services in the applicationenvironment can interact in various ways to provide a set of cohesiveuser interactions with the application, such as a set of user servicesapplicable to end user customers.

Application Intelligence Modeling: Entities in the applicationenvironment (such as the JBoss service, MQSeries modules, and databases)and the services provided by the entities (such as a login transaction,service or product search, or purchase transaction) may be mapped to anapplication intelligence model. In the application intelligence model, abusiness transaction represents a particular service provided by themonitored environment. For example, in an e-commerce application,particular real-world services can include a user logging in, searchingfor items, or adding items to the cart. In a content portal, particularreal-world services can include user requests for content such assports, business, or entertainment news. In a stock trading application,particular real-world services can include operations such as receivinga stock quote, buying, or selling stocks.

Business Transactions: A business transaction representation of theparticular service provided by the monitored environment provides a viewon performance data in the context of the various tiers that participatein processing a particular request. A business transaction, which mayeach be identified by a unique business transaction identification (ID),represents the end-to-end processing path used to fulfill a servicerequest in the monitored environment (e.g., adding items to a shoppingcart, storing information in a database, purchasing an item online,etc.). Thus, a business transaction is a type of user-initiated actionin the monitored environment defined by an entry point and a processingpath across application servers, databases, and potentially many otherinfrastructure components. Each instance of a business transaction is anexecution of that transaction in response to a particular user request(e.g., a socket call, illustratively associated with the TCP layer). Abusiness transaction can be created by detecting incoming requests at anentry point and tracking the activity associated with request at theoriginating tier and across distributed components in the applicationenvironment (e.g., associating the business transaction with a 4-tupleof a source IP address, source port, destination IP address, anddestination port). A flow map can be generated for a businesstransaction that shows the touch points for the business transaction inthe application environment. In one embodiment, a specific tag may beadded to packets by application specific agents for identifying businesstransactions (e.g., a custom header field attached to a hypertexttransfer protocol (HTTP) payload by an application agent, or by anetwork agent when an application makes a remote socket call), such thatpackets can be examined by network agents to identify the businesstransaction identifier (ID) (e.g., a Globally Unique Identifier (GUID)or Universally Unique Identifier (UUID)).

Performance monitoring can be oriented by business transaction to focuson the performance of the services in the application environment fromthe perspective of end users. Performance monitoring based on businesstransactions can provide information on whether a service is available(e.g., users can log in, check out, or view their data), response timesfor users, and the cause of problems when the problems occur.

A business application is the top-level container in the applicationintelligence model. A business application contains a set of relatedservices and business transactions. In some implementations, a singlebusiness application may be needed to model the environment. In someimplementations, the application intelligence model of the applicationenvironment can be divided into several business applications. Businessapplications can be organized differently based on the specifics of theapplication environment. One consideration is to organize the businessapplications in a way that reflects work teams in a particularorganization, since role-based access controls in the Controller UI areoriented by business application.

A node in the application intelligence model corresponds to a monitoredserver or JVM in the application environment. A node is the smallestunit of the modeled environment. In general, a node corresponds to anindividual application server, JVM, or Common Language Runtime (CLR) onwhich a monitoring Agent is installed. Each node identifies itself inthe application intelligence model. The Agent installed at the node isconfigured to specify the name of the node, tier, and businessapplication under which the Agent reports data to the Controller.

Business applications contain tiers, the unit in the applicationintelligence model that includes one or more nodes. Each node representsan instrumented service (such as a web application). While a node can bea distinct application in the application environment, in theapplication intelligence model, a node is a member of a tier, which,along with possibly many other tiers, make up the overall logicalbusiness application.

Tiers can be organized in the application intelligence model dependingon a mental model of the monitored application environment. For example,identical nodes can be grouped into a single tier (such as a cluster ofredundant servers). In some implementations, any set of nodes, identicalor not, can be grouped for the purpose of treating certain performancemetrics as a unit into a single tier.

The traffic in a business application flows among tiers and can bevisualized in a flow map using lines among tiers. In addition, the linesindicating the traffic flows among tiers can be annotated withperformance metrics. In the application intelligence model, there maynot be any interaction among nodes within a single tier. Also, in someimplementations, an application agent node cannot belong to more thanone tier.

Similarly, a machine agent cannot belong to more than one tier. However,more than one machine agent can be installed on a machine.

A backend is a component that participates in the processing of abusiness transaction instance. A backend is not instrumented by anagent. A backend may be a web server, database, message queue, or othertype of service. The agent recognizes calls to these backend servicesfrom instrumented code (called exit calls). When a service is notinstrumented and cannot continue the transaction context of the call,the agent determines that the service is a backend component. The agentpicks up the transaction context at the response at the backend andcontinues to follow the context of the transaction from there.

Performance information is available for the backend call. For detailedtransaction analysis for the leg of a transaction processed by thebackend, the database, web service, or other application need to beinstrumented.

The application intelligence platform uses both self-learned baselinesand configurable thresholds to help identify application issues. Acomplex distributed application has a large number of performancemetrics and each metric is important in one or more contexts. In suchenvironments, it is difficult to determine the values or ranges that arenormal for a particular metric; set meaningful thresholds on which tobase and receive relevant alerts; and determine what is a “normal”metric when the application or infrastructure undergoes change. Forthese reasons, the disclosed application intelligence platform canperform anomaly detection based on dynamic baselines or thresholds.

The disclosed application intelligence platform automatically calculatesdynamic baselines for the monitored metrics, defining what is “normal”for each metric based on actual usage. The application intelligenceplatform uses these baselines to identify subsequent metrics whosevalues fall out of this normal range. Static thresholds that are tediousto set up and, in rapidly changing application environments,error-prone, are no longer needed.

The disclosed application intelligence platform can use configurablethresholds to maintain service level agreements (SLAs) and ensureoptimum performance levels for system by detecting slow, very slow, andstalled transactions. Configurable thresholds provide a flexible way toassociate the right business context with a slow request to isolate theroot cause.

In addition, health rules can be set up with conditions that use thedynamically generated baselines to trigger alerts or initiate othertypes of remedial actions when performance problems are occurring or maybe about to occur.

For example, dynamic baselines can be used to automatically establishwhat is considered normal behavior for a particular application.Policies and health rules can be used against baselines or other healthindicators for a particular application to detect and troubleshootproblems before users are affected. Health rules can be used to definemetric conditions to monitor, such as when the “average response time isfour times slower than the baseline”. The health rules can be createdand modified based on the monitored application environment.

Examples of health rules for testing business transaction performancecan include business transaction response time and business transactionerror rate. For example, health rule that tests whether the businesstransaction response time is much higher than normal can define acritical condition as the combination of an average response timegreater than the default baseline by 3 standard deviations and a loadgreater than 50 calls per minute. In some implementations, this healthrule can define a warning condition as the combination of an averageresponse time greater than the default baseline by 2 standard deviationsand a load greater than 100 calls per minute. In some implementations,the health rule that tests whether the business transaction error rateis much higher than normal can define a critical condition as thecombination of an error rate greater than the default baseline by 3standard deviations and an error rate greater than 10 errors per minuteand a load greater than 50 calls per minute. In some implementations,this health rule can define a warning condition as the combination of anerror rate greater than the default baseline by 2 standard deviationsand an error rate greater than 5 errors per minute and a load greaterthan 50 calls per minute. These are non-exhaustive and non-limitingexamples of health rules and other health rules can be defined asdesired by the user.

Policies can be configured to trigger actions when a health rule isviolated or when any event occurs. Triggered actions can includenotifications, diagnostic actions, auto-scaling capacity, runningremediation scripts.

Most of the metrics relate to the overall performance of the applicationor business transaction (e.g., load, average response time, error rate,etc.) or of the application server infrastructure (e.g., percentage CPUbusy, percentage of memory used, etc.). The Metric Browser in thecontroller UI can be used to view all of the metrics that the agentsreport to the controller.

In addition, special metrics called information points can be created toreport on how a given business (as opposed to a given application) isperforming. For example, the performance of the total revenue for acertain product or set of products can be monitored. Also, informationpoints can be used to report on how a given code is performing, forexample how many times a specific method is called and how long it istaking to execute. Moreover, extensions that use the machine agent canbe created to report user defined custom metrics. These custom metricsare base-lined and reported in the controller, just like the built-inmetrics.

All metrics can be accessed programmatically using a RepresentationalState Transfer (REST) API that returns either the JavaScript ObjectNotation (JSON) or the eXtensible Markup Language (XML) format. Also,the REST API can be used to query and manipulate the applicationenvironment.

Snapshots provide a detailed picture of a given application at a certainpoint in time. Snapshots usually include call graphs that allow thatenables drilling down to the line of code that may be causingperformance problems. The most common snapshots are transactionsnapshots.

FIG. 4 illustrates an example application intelligence platform (system)400 for performing one or more aspects of the techniques herein. Thesystem 400 in FIG. 4 includes client device 405 and 492, mobile device415, network 420, network server 425, application servers 430, 440, 450,and 460, asynchronous network machine 470, data stores 480 and 485,controller 490, and data collection server 495. The controller 490 caninclude visualization system 496 for providing displaying of the reportgenerated for performing the field name recommendations for fieldextraction as disclosed in the present disclosure. In someimplementations, the visualization system 496 can be implemented in aseparate machine (e.g., a server) different from the one hosting thecontroller 490.

Client device 405 may include network browser 410 and be implemented asa computing device, such as for example a laptop, desktop, workstation,or some other computing device. Network browser 410 may be a clientapplication for viewing content provided by an application server, suchas application server 430 via network server 425 over network 420.

Network browser 410 may include agent 412. Agent 412 may be installed onnetwork browser 410 and/or client 405 as a network browser add-on,downloading the application to the server, or in some other manner.Agent 412 may be executed to monitor network browser 410, the operatingsystem of client 405, and any other application, API, or anothercomponent of client 405. Agent 412 may determine network browsernavigation timing metrics, access browser cookies, monitor code, andtransmit data to data collection 495, controller 490, or another device.Agent 412 may perform other operations related to monitoring a requestor a network at client 405 as discussed herein including reportgenerating.

Mobile device 415 is connected to network 420 and may be implemented asa portable device suitable for sending and receiving content over anetwork, such as for example a mobile phone, smart phone, tabletcomputer, or other portable device. Both client device 405 and mobiledevice 415 may include hardware and/or software configured to access aweb service provided by network server 425.

Mobile device 415 may include network browser 417 and an agent 419.Mobile device may also include client applications and other code thatmay be monitored by agent 419. Agent 419 may reside in and/orcommunicate with network browser 417, as well as communicate with otherapplications, an operating system, APIs and other hardware and softwareon mobile device 415. Agent 419 may have similar functionality as thatdescribed herein for agent 412 on client 405, and may report data todata collection server 495 and/or controller 490.

Network 420 may facilitate communication of data among differentservers, devices and machines of system 400 (some connections shown withlines to network 420, some not shown). The network may be implemented asa private network, public network, intranet, the Internet, a cellularnetwork, Wi-Fi network, VoIP network, or a combination of one or more ofthese networks. The network 420 may include one or more machines such asload balance machines and other machines.

Network server 425 is connected to network 420 and may receive andprocess requests received over network 420. Network server 425 may beimplemented as one or more servers implementing a network service, andmay be implemented on the same machine as application server 430 or oneor more separate machines. When network 420 is the Internet, networkserver 425 may be implemented as a web server.

Application server 430 communicates with network server 425, applicationservers 440 and 450, and controller 490. Application server 450 may alsocommunicate with other machines and devices (not illustrated in FIG. 4). Application server 430 may host an application or portions of adistributed application. The host application 432 may be in one of manyplatforms, such as including a Java, PHP, .Net, and Node.JS, beimplemented as a Java virtual machine, or include some other host type.Application server 430 may also include one or more agents 434 (i.e.,“modules”), including a language agent, machine agent, and networkagent, and other software modules. Application server 430 may beimplemented as one server or multiple servers as illustrated in FIG. 4 .

Application 432 and other software on application server 430 may beinstrumented using byte code insertion, or byte code instrumentation(BCI), to modify the object code of the application or other software.The instrumented object code may include code used to detect callsreceived by application 432, calls sent by application 432, andcommunicate with agent 434 during execution of the application. BCI mayalso be used to monitor one or more sockets of the application and/orapplication server in order to monitor the socket and capture packetscoming over the socket.

In some embodiments, server 430 may include applications and/or codeother than a virtual machine. For example, servers 430, 440, 450, and460 may each include Java code, .Net code, PHP code, Ruby code, C code,C++ or other binary code to implement applications and process requestsreceived from a remote source. References to a virtual machine withrespect to an application server are intended to be for exemplarypurposes only.

Agents 434 on application server 430 may be installed, downloaded,embedded, or otherwise provided on application server 430. For example,agents 434 may be provided in server 430 by instrumentation of objectcode, downloading the agents to the server, or in some other manner.Agent 434 may be executed to monitor application server 430, monitorcode running in a virtual machine 432 (or other program language, suchas a PHP, .Net, or C program), machine resources, network layer data,and communicate with byte instrumented code on application server 430and one or more applications on application server 430.

Each of agents 434, 444, 454, and 464 may include one or more agents,such as language agents, machine agents, and network agents. A languageagent may be a type of agent that is suitable to run on a particularhost. Examples of language agents include a Java agent, .Net agent, PHPagent, and other agents. The machine agent may collect data from aparticular machine on which it is installed. A network agent may capturenetwork information, such as data collected from a socket.

Agent 434 may detect operations such as receiving calls and sendingrequests by application server 430, resource usage, and incomingpackets. Agent 434 may receive data, process the data, for example byaggregating data into metrics, and transmit the data and/or metrics tocontroller 490. Agent 434 may perform other operations related tomonitoring applications and application server 430 as discussed herein.For example, agent 434 may identify other applications, share businesstransaction data, aggregate detected runtime data, and other operations.

An agent may operate to monitor a node, tier of nodes, or other entity.A node may be a software program or a hardware component (e.g., memory,processor, and so on). A tier of nodes may include a plurality of nodeswhich may process a similar business transaction, may be located on thesame server, may be associated with each other in some other way, or maynot be associated with each other.

A language agent may be an agent suitable to instrument or modify,collect data from, and reside on a host. The host may be a Java, PHP,.Net, Node.JS, or other type of platform. Language agents may collectflow data as well as data associated with the execution of a particularapplication. The language agent may instrument the lowest level of theapplication to gather the flow data. The flow data may indicate whichtier is communicating with which tier and on which port. In someinstances, the flow data collected from the language agent includes asource IP, a source port, a destination IP, and a destination port. Thelanguage agent may report the application data and call chain data to acontroller. The language agent may report the collected flow dataassociated with a particular application to a network agent.

A network agent may be a standalone agent that resides on the host andcollects network flow group data. The network flow group data mayinclude a source IP, destination port, destination IP, and protocolinformation for network flow received by an application on which networkagent is installed. The network agent may collect data by interceptingand performing packet capture on packets coming in from one or morenetwork interfaces (e.g., so that data generated/received by all theapplications using sockets can be intercepted). The network agent mayreceive flow data from a language agent that is associated withapplications to be monitored. For flows in the flow group data thatmatch flow data provided by the language agent, the network agent rollsup the flow data to determine metrics such as TCP throughput, TCP loss,latency, and bandwidth. The network agent may then report the metrics,flow group data, and call chain data to a controller. The network agentmay also make system calls at an application server to determine systeminformation, such as for example a host status check, a network statuscheck, socket status, and other information.

A machine agent, which may be referred to as an infrastructure agent,may reside on the host and collect information regarding the machinewhich implements the host. A machine agent may collect and generatemetrics from information such as processor usage, memory usage, andother hardware information.

Each of the language agent, network agent, and machine agent may reportdata to the controller. Controller 490 may be implemented as a remoteserver that communicates with agents located on one or more servers ormachines. The controller may receive metrics, call chain data and otherdata, correlate the received data as part of a distributed transaction,and report the correlated data in the context of a distributedapplication implemented by one or more monitored applications andoccurring over one or more monitored networks. The controller mayprovide reports, one or more user interfaces, and other information fora user.

Agent 434 may create a request identifier for a request received byserver 430 (for example, a request received by a client 405 or 415associated with a user or another source). The request identifier may besent to client 405 or mobile device 415, whichever device sent therequest. In embodiments, the request identifier may be created when datais collected and analyzed for a particular business transaction.

Each of application servers 440, 450, and 460 may include an applicationand agents. Each application may run on the corresponding applicationserver. Each of applications 442, 452, and 462 on application servers440-460 may operate similarly to application 432 and perform at least aportion of a distributed business transaction. Agents 444, 454, and 464may monitor applications 442-462, collect and process data at runtime,and communicate with controller 490. The applications 432, 442, 452, and462 may communicate with each other as part of performing a distributedtransaction. Each application may call any application or method ofanother virtual machine.

Asynchronous network machine 470 may engage in asynchronouscommunications with one or more application servers, such as applicationserver 450 and 460. For example, application server 450 may transmitseveral calls or messages to an asynchronous network machine. Ratherthan communicate back to application server 450, the asynchronousnetwork machine may process the messages and eventually provide aresponse, such as a processed message, to application server 460.Because there is no return message from the asynchronous network machineto application server 450, the communications among them areasynchronous.

Data stores 480 and 485 may each be accessed by application servers suchas application server 460. Data store 485 may also be accessed byapplication server 450. Each of data stores 480 and 485 may store data,process data, and return queries received from an application server.Each of data stores 480 and 485 may or may not include an agent.

Controller 490 may control and manage monitoring of businesstransactions distributed over application servers 430-460. In someembodiments, controller 490 may receive application data, including dataassociated with monitoring client requests at client 405 and mobiledevice 415, from data collection server 495. In some embodiments,controller 490 may receive application monitoring data and network datafrom each of agents 412, 419, 434, 444, and 454 (also referred to hereinas “application monitoring agents”). Controller 490 may associateportions of business transaction data, communicate with agents toconfigure collection of data, and provide performance data and reportingthrough an interface. The interface may be viewed as a web-basedinterface viewable by client device 492, which may be a mobile device,client device, or any other platform for viewing an interface providedby controller 490. In some embodiments, a client device 492 may directlycommunicate with controller 490 to view an interface for monitoringdata.

Client device 492 may include any computing device, including a mobiledevice or a client computer such as a desktop, work station or othercomputing device. Client computer 492 may communicate with controller490 to create and view a custom interface. In some embodiments,controller 490 provides an interface for creating and viewing the custominterface as a content page, e.g., a web page, which may be provided toand rendered through a network browser application on client device 492.

Applications 432, 442, 452, and 462 may be any of several types ofapplications. Examples of applications that may implement applications432-462 include a Java, PHP, .Net, Node.JS, and other applications.

FIG. 5 is a block diagram of a computer system 500 for implementing thepresent technology, which is a specific implementation of device 200 ofFIG. 2 above. System 500 of FIG. 5 may be implemented in the contexts ofthe likes of clients 405, 492, network server 425, servers 430, 440,450, 460, asynchronous network machine 470, and controller 490 of FIG. 4. (Note that the specifically configured system 500 of FIG. 5 and thecustomized device 200 of FIG. 2 are not meant to be mutually exclusive,and the techniques herein may be performed by any suitably configuredcomputing device.)

The computing system 500 of FIG. 5 includes one or more processors 510and memory 520. Main memory 520 stores, in part, instructions and datafor execution by processor 510. Main memory 520 can store the executablecode when in operation. The system 500 of FIG. 5 further includes a massstorage device 530, portable storage medium drive(s) 540, output devices550, user input devices 560, a graphics display 570, and peripheraldevices 580.

The components shown in FIG. 5 are depicted as being connected via asingle bus 590. However, the components may be connected through one ormore data transport means. For example, processor unit 510 and mainmemory 520 may be connected via a local microprocessor bus, and the massstorage device 530, peripheral device(s) 580, portable or remote storagedevice 540, and display system 570 may be connected via one or moreinput/output (I/O) buses.

Mass storage device 530, which may be implemented with a magnetic diskdrive or an optical disk drive, is a non-volatile storage device forstoring data and instructions for use by processor unit 510. Massstorage device 530 can store the system software for implementingembodiments of the present disclosure for purposes of loading thatsoftware into main memory 520.

Portable storage device 540 operates in conjunction with a portablenon-volatile storage medium, such as a compact disk, digital video disk,magnetic disk, flash storage, etc. to input and output data and code toand from the computer system 500 of FIG. 5 . The system software forimplementing embodiments of the present disclosure may be stored on sucha portable medium and input to the computer system 500 via the portablestorage device 540.

Input devices 560 provide a portion of a user interface. Input devices560 may include an alpha-numeric keypad, such as a keyboard, forinputting alpha-numeric and other information, or a pointing device,such as a mouse, a trackball, stylus, or cursor direction keys.Additionally, the system 500 as shown in FIG. 5 includes output devices550. Examples of suitable output devices include speakers, printers,network interfaces, and monitors.

Display system 570 may include a liquid crystal display (LCD) or othersuitable display device. Display system 570 receives textual andgraphical information, and processes the information for output to thedisplay device.

Peripherals 580 may include any type of computer support device to addadditional functionality to the computer system. For example, peripheraldevice(s) 580 may include a modem or a router.

The components contained in the computer system 500 of FIG. 5 caninclude a personal computer, hand held computing device, telephone,mobile computing device, workstation, server, minicomputer, mainframecomputer, or any other computing device. The computer can also includedifferent bus configurations, networked platforms, multi-processorplatforms, etc. Various operating systems can be used including Unix,Linux, Windows, Apple OS, and other suitable operating systems,including mobile versions.

When implementing a mobile device such as smart phone or tabletcomputer, the computer system 500 of FIG. 5 may include one or moreantennas, radios, and other circuitry for communicating over wirelesssignals, such as for example communication using Wi-Fi, cellular, orother wireless signals.

Agent Profiler

As noted above, many software tools, including performance monitoringsystems, make use of software agents, such as Java Agents, to provideinstrumentation capabilities to an application. However, there arecurrently no mechanisms to measure the resource overhead of softwareagents, or the actions of the agents, or identifying potential conflictsbetween multiple agents.

The techniques herein, therefore, provide for an agent profiler tomonitor activities and performance of software agents. In particular, inone embodiment, an “Agent Profiler” is designed to deeply audit,monitor, and stress test any software agent (e.g., Java Agent or anyother interface for a given runtime to instrument that runtime) to helpoptimize and identify performance issues, as well as exposing conflictsbetween multiple running agents. Currently, there is no other tool thatcreates this “container” type system that would allow the kind ofdetailed monitoring and assessment.

As described in greater detail below, the illustrative Agent Profilerherein creates a “container” (or “harness environment”) for Javainstrumentation which acts as a proxy for all software agent calls inand out of the Java instrumentation system. (Notably, though thedescription herein frequently references “Java” for agents,instrumentation, runtime, etc., other suitable platforms with softwareagents may also be used according to the techniques herein.) Thetechniques herein can run and monitor (manage and profile) theperformance of any agent and multiple agents, having the ability tostart and stop, as well as monitor and even prevent any conflictsbetween agents. For example, software agents may do any number ofactions, such as application performance monitoring, dev ops, security,runtime application self-protection (RASP), etc. That is, by monitoringthe proxy-based instrumentation interface (e.g., the Javainstrumentation interface), the techniques herein can control thesoftware agents (start/stop, prevent, roll-back, etc.), determine theresource overhead for any of these software agents (time actions,monitor and correlate resource utilizations, etc.) and complete an auditof all of the actions by these agents, accordingly.

Operationally, the techniques herein create an “instrumentationcontainer” system that wraps around one or more software agents (e.g.,Java Agents). Normally, for an application, a javaagent switch is usedto launch a software agent and that launched agent talks directly to theJVM. With reference generally to FIGS. 6A-6B, the techniques hereinadjust the relationship between an application (JVM) 610 and its agents620 to start the Agent Profiler 630 as the only javaagent started withthe switch, and then the Agent Profiler itself has the ability to startone or more software agents 620 (on demand), and then monitors theagents and can either grant or deny access to the JVM instrumentationsystem as it monitors it. (Note that in one embodiment, a configurationsetting may allow a choice as to whether the profiler is launched as aseparate thread versus in the JVM premain calling thread.)

As shown in FIG. 6A, the techniques herein are directed at monitoring,assessing, and controlling an application 610′s software agent(s)(application agent(s)) 620 by “boxing” the agent(s) into a “container”of the agent profiler 630 so that the profiler can measure the resourceusage of the agent(s). Shown differently in FIG. 6B, this “container” isessentially created by the configuration of the instrumentationinterfaces 640 (e.g., for Java Agents in particular, the“java.lang.instrument.Instrumentation” interface), where the AgentProfiler 630 provides an Instrumentation Handle to a “proxy”implementation 642 (versus the real JVM instrumentation handle 640) tothe software agent(s) 620, and essentially starts up the softwareagent(s) in a similar manner to how the JVM would start them. Notably,this proxy operation is completely transparent to the software agent(s),where each call made by an agent to the instrumentation implementation640 is actually made to the Agent Profiler 630 (via the proxyinstrumentation 642), which can then register the activities of theagent and also monitor situations with multiple agents (e.g.,particularly when changing the same class files), as described ingreater detail below. That is, by monitoring calls to that interface(intercepting calls on the proxy implementation interface 642), theAgent Profiler 630 can achieve granular visibility into aspects of asoftware agent of any kind and can control the interactions with the JVMas well.

Specifically, key features of the Agent Profiler defined herein, notablywithout requiring any changes to the software agent (e.g., Java Agent)code, may include:

Tracing/Recording/Enabling software agent instrumentation activities,such as: general calls to the instrumentation API (e.g., the JVMinstrumentation API—java.lang.instrument.Instrumentation), calls toretransform classes, changes made to classes in the transform,getAllLoadedClasses call, etc.;

Monitoring of all individual agent thread activities, such as: CPU,memory allocation, lock waits, block count, time, etc.;

Enabling/Disabling instrumentation, for things such as: CPU timetracking, lock contention tracking,

Determining differences in instrumented classes, for example:determining a recorded original class, determining a recorded modifiedclass, determining differences between the instrumentation, identifyingconflicts between multiple software agents (e.g., one changes a class,another changes the class back, etc.), providing arbitration/conflictresolution between multiple software agents (e.g., prioritizing one overthe other or otherwise);

Determining class loader hierarchy (e.g., identifying agent loader anddelegation chains);

Classifying general JVM health, such as through monitoring: garbagecollection and duration, CPU, Heap and NonHeap, Metaspace, thread countand creation rate, etc.; and

Stack Sampling, as described below (e.g., detecting threads, threadstatus, hotspots, etc.).

As context on software agents in general, and Java Agents in particular(as an example throughout), a software agent is designed to be launchedas part of an application usually for the purpose of monitoring,logging, security, and so on. In general, a “bootstrap” class subset ispackaged into a jar file which has a manifest that describes theavailable functions as well as appending to existing application andsystem classpaths. Upon startup of the underlying application, there istypically a switch (for example, a -javaagent:PATH:javaagent.jar switch)that causes the runtime to give control to the agent class specified inthe manifest and calls the “premain” method called and passing the“Instrumentation Handle” and associated parameters. Once control isgiven, the premain method would generally create a “transformer” wherethe classes can be modified (i.e., where an agent provides animplementation of this interface in order to transform classfiles—before the class is defined by the JVM) which will receive classbytes as they are loaded via the transform method. At this point, thetransform method can return the original bytes or modified bytes thatmight contain instrumentation. (Note that “retransforming” is causing aclass to go through a transformer so the class can be modified.)

The Agent Profiler 630 is illustratively a special kind of softwareagent (e.g., a Java Agent) that discovers managed software agents 620for an application 610 by reading the descriptions in the configurationsfile of the application, and then launches the discovered agents in amanaged (proxy) environment where all requests made by an agent actuallygo directly to the Agent Profiler (proxy instrumentation) versus the JVM(application instrumentation). In particular, as mentioned above, theAgent Profiler 630 provides the instrumentation ecosystem for themanaged agents and acts as an “Instrumentation Proxy”, either passingthe instrumentation (modifications, transformations, etc.) to the JVM orblocking it depending on configuration. In addition, these softwareagent activities are audited, and the agent profiler containsinstrumentation of its own to monitor metrics and basic vital signs ofthe software agents and JVM as well as to perform garbage collection andto detect and mitigate locking issues or other compatibility issuesbetween different software agents, each as described below. In otherwords, the agent profiler can act as a management system, providing highvisibility into the operations of a software agent (e.g., Java Agent).

In detail, to add a software agent to the Agent Profiler, the AgentProfiler may first be attached to the application via a javaagent switch(e.g., -javaagent:../AgentProfiler/prod/lib/agentProfilerPremain.jar),and then any of the software agents that are in the application startupare removed (if any are preconfigured there). Any configuration changesmay be set (as described below), and then the software agents areconverted to be used in the Agent Profiler proxy environment, and theAgent Profiler is started.

An example of this is shown in FIGS. 7A-7B, where an example softwareagent called “Example” (code 710) is shown in FIG. 7A, which would beconverted by a switch on a startup line to the code 720 shown in FIG.7B. In general, the changes made (shown in bold/underlined text in FIGS.7A-7B) result in the agent pointing to the Agent Profiler as shown inFIG. 7B (along with configuration settings), rather than the original“TestApp” as shown in FIG. 7A. Notably, as shown:

-   -   javaagent: the name of the agent jar file with the premain        entry;    -   Installed: Fully Qualified directory path to the agent jar file;    -   name: Unique Managed Agent name to be referred to;    -   params: Additional javaagent parameters to be passed;    -   loaded: Whether or not to load;    -   properties: additional java agent properties either as        -Dkey=value or command separated key=value (note that properties        already exist in the startup line, they do not have to moved        here);    -   addbootjar: additional jar to add to the boot classpath;    -   addpackages: additional packages that belong to this agent;    -   active: whether or not the agent is active (i.e., toggle agents        within environment);    -   getloaded-classes: Allows all loaded classes to be sent to this        agent OR an empty list;    -   redefine-classes: Allows for the use of redefineClasses API by        this agent;    -   transform-classes: Allows transforms by this agent;    -   modify-classes: Allows transforms, but NOT class byte changes by        this agent; and    -   retransform-classes: Allows retransforms to be generated by this        agent.

According to the techniques herein, the agent profiler, thoughexceptional at measuring the performance of Java Agents, also has manyfeatures that are focused on the JVM in general. For example, thefollowing commands are made available through an interface with theagent profiler, where the agent profiler may determine and/or controlthe following standard actions through the proxy interface defined above(e.g., for automated API activity, or else in response to a userinterface command):

Show All Threads—show all threads;

Show Runtime—show the Java runtime info;

Show Properties—show properties and environmental variables;

Show Network—show current network interfaces;

Show Classloaders—show all classloaders and associated classes;

Full Garbage Collection—force a full garbage collection (gc);

Create Heap Dump—creates a heap dump;

Show Loaded Class—show loaded class with method, options: class=class[&method=method];

Show Thread Local—show all Thread Local storage;

Show Memory—show current heap memory; and

Show File—show file, options: showfile=file.

In addition, according to the techniques herein, the agent profiler alsoprovides agent profiler dependent operations, enabling/disabling of JVMinstrumentation, management of software agents, visibility into files,visibility into locking, visibility into garbage collection, classinstrumentation, and “stack sampling”, each of which being describedfurther below.

For instance, regarding agent profiler dependent operations, thetechniques herein may show an agent profiler log, show the profiler'scurrent configuration, and can allow browsing the agent profiler files(e.g., as a file/director interface or otherwise), such as .yal files,.jke files, .log files, libraries, and so on. In addition, agentprofiler statistics (stats) may be collected and shared (reported,displayed, analyzed, etc.), such as a table 800 shown in FIG. 8 ,showing various metrics 810 and values 820, e.g., “ActivityEvents”,“CPUTimeEnabled”, “Current Loaded Classes”, “Current Threads”, and soon. Furthermore, other profiler operations may include stopping the JVM(shutting down the Agent Profiler JVM), launching shell scriptoperation(s) (e.g., adding &count=count and/or &delay=sec to executemultiple times with a delay), setting a Help Port to echo back if behinda proxy or firewall, etc.

In addition, in one embodiment, the agent profiler herein canenable/disable JVM instrumentation, such as by enabling or disablingthread CPU time tracking (e.g., via “?cputime=truelfalse”), and/or byenabling or disabling lock contention tracking (e.g., via“?lockcontention=truelfalse”).

Additional embodiments of the agent profiler allow for the generalmanagement of software agents. For instance, in one embodiment, theagent profiler can provide/show a list of all managed agents, i.e., allagents within the proxy instrumentation environment described above, aswell as all of their associated files (e.g., .jar files, configurationfiles, etc.). Further, the techniques herein can show cached agentactivity or activity logs, either for all managed agents or only one ormore named agents (e.g., launching, loading jars, adding jars, etc.), aswell as showing current system properties/settings for the agents. Asshown in FIG. 9 , the techniques herein may also show agent threads,such as in a display 900 indicating a number of threads (name 910, ID920), their state 930 (e.g., TIMED_WAITING, WAITING, etc.), CPUutilization (max) 940, various counters 950, timers 960, thread location970, and so on. (Display 900 is not meant to be limiting to the presentdisclosure, and is merely an example of various information regardingthreads of particular software agents, accordingly.)

More than merely showing files of the software agents (e.g., in adirectory), the techniques herein can also show aspects of threadlocking, such as showing current deadlocks, showing threads owning locksand threads waiting on those locks (FIG. 10 ), as well as showing allthreads waiting on locks (FIG. 11 ). FIG. 10 , for example, shows atable 1000 listing a number of threads by name 1010, ID 1020, and theircorresponding state 1030, where the table also indicates whether thethread owns a lock 1040 and those other threads 1050 waiting on thatlock. Conversely, in FIG. 11 , table 1100 shows threads by name 1110 andID 1120, state 1030 and a LockName 1140 of the lock they are waiting on,as well as other metrics 1150, such as how long the wait has been, andso on.

The techniques herein may also provide insight into garbage collection,such as showing all instances of garbage collection activity, such asscavenging allocation failures and metadata, as well as associatedmetrics, such as time, duration, reduction in memory size, etc. Inaddition, the techniques herein can also show garbage collectionstatistics, such as average and maximum durations, average and maximumcollections, and so on for each type of garbage collection. Othercommands regarding garbage collection are also made available by theagent profiler herein, such as allowing a thread to start to use HeapSpace or Metaspace Space (with settings for delay, size or percent,etc.), to show used memory (Heap and Metaspace), and to clear Heap orMetaspace memory created.

According to one or more embodiments of the present disclosure,instrumented classes can be detected, shown, and processed by the AgentProfiler herein. For instance, FIG. 12 illustrates an example table 1200of classes instrumented by managed agents, where each entry has a time“LastInstrumented” 1210, a number of “TimesInstrumented” 1220, an“AgentsModifying” 1230 for the associated Class 1240. Other informationmay also be included, such as the Loader 1250 used, the OldBytesSize1260 and NewBytesSize 1270, a list of actual changes 1280 to the class,and so on. Additionally, FIG. 13 illustrates another table 1300 showingretransformed classes (showing all classes retransformed by managedagents), and may identify the class 1310, the Loader 1320, and thenumber of times 1330 the class was retransformed. Other classinstrumentation controls are provided herein as well, such as, e.g.,“Uninstrument All” classes, “Reinstrument All” classes, or “RetransformAll” classes (including for each whether to log each class, add a delaybetween each class, ignore certain instrumentation filtering, etc.).Controls may also include showing all modified classes, showing alloriginal classes, and so on.

As mentioned above, one or more embodiments herein may provide for“Stack Sampling”, optionally as an alternative to instrumentation.Though stack sampling, in and of itself, is a known technique, theembodiments herein refine stack sampling to provide a more accuratemethodology over current mechanisms. In particular, though current stacksampling examines all stacks for all threads, the techniques hereinprovide greater accuracy by intelligently determining (and thenfiltering to) which stacks and/or threads are related to transactions,and further focusing on which stacks and threads are actually active(since some indicators do not definitively indicate activity: e.g.,RUNNABLE does not always equal active, and is always shown when innative mode).

Said differently, the “stack sampler” techniques herein are specificallyable to filter out samples by:

-   -   Only choosing transactions by looking at the Thread Name;    -   Only choosing transactions by looking at what is in the call        stack; and    -   Eliminating non-active transactions by recognizing class/methods        that indicate it is idle in the Thread Pool.        The end result is something that is “razor accurate” in        eliminating noise—unlike conventional samplers.

According to one or more embodiments of the techniques herein, the agentprofiler stack sampler may be tuned for the application, specifically.That is, one objective of a stack sampler is to take stack trace samplesat a periodic interval and to somehow indicate areas where theapplication is using CPU by the frequency in which a method and linenumber shows up as being current and the frequency that a thread showsup being actively running. However, since it is difficult to effectivelydetermine this information, the agent profiler sampler containsconfiguration settings to fine-tune this process.

For instance, in one embodiment herein, the techniques may also usethread names to determine if a transaction is occurring. That is, inmost cases, the threads of interest will be the transaction threads asthey are the most active and the most important—for this reason, it isimportant to isolate those threads. The techniques herein, therefore,take advantage of the fact that most application servers use a standardnaming convention for the threads, which can be used in the followingconfiguration filter:

-   -   sampler-filter-threads: prefix, prefix, etc.; and    -   sampler-filter-thread-include: truelfalse (set to either include        or exclude what is in the sampler-filter-threads configuration).

In addition, in one embodiment, the techniques herein may use stackcontents to determine if an event is a transaction. For instance, theremay also be times when a transaction must be identified by the stackcontents such as a class/method combination using a filter, such as, forexample:

-   -   sampler-filter-stacks: package.class.method,        package.class.method, etc.    -   sampler-filter-stack-include: truelfalse (set to either include        or exclude what is in the sampler-filter-stacks configuration).

The techniques herein are also particularly helpful in determining if athread is active. Specifically, since there is limited utility insampling a thread that is not actively attempting to execute atransaction, the techniques herein may exclude these threads from thesampling process. For example, Java has the following thread statesavailable:

-   -   NEW—A thread that has not yet started is in this state;    -   RUNNABLE—A thread executing in the Java virtual machine is in        this state;    -   BLOCKED—A thread that is blocked waiting for a monitor lock is        in this state;    -   WAITING—A thread that is waiting indefinitely for another thread        to perform a particular action is in this state;    -   TIMED_WAITING—A thread that is waiting for another thread to        perform an action for up to a specified waiting time is in this        state; and    -   TERMINATED—A thread that has exited is in this state.        Accordingly, the only states that could be entered during a        transaction would be: RUNNABLE, BLOCKED, WAITING, and        TIMED_WAITING. Notably, the RUNNABLE state can be somewhat        misleading as all executions in native code will have this        state, and the WAITING and TIMED_WAITING states are often merely        threads in a thread pool waiting to be used. Also, the BLOCKED        state is the one state that should not be seen in back-to-back        samples, which would normally indicate a real issue is at hand.        However, there are certain methods that always indicate that the        thread is idle, such as, e.g., (currently):    -   com.ibm.io.async.AsyncLibrary.aio_getioev2,    -   sun.misc.Unsafe.park,    -   java.lang.Object.wait,    -   java.net.PlainSocketImpl.socketAccept,    -   sun.nio.ch.KQueueArrayWrapper.kevent0,    -   java.lang.Thread.sleep,    -   java.util.concurrent.locks.LockSupport.park, and    -   java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awa        it.        As such, by setting the following property, the sampler can be        instructed to NOT sample any threads that are currently        executing these “idle” methods:    -   sampler-considered-idle-stack-contents:    -   java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await.

In general, the Agent Profiler has numerous configuration settings tocontrol how it performs and manages the software agents. For instance,in addition to several core configuration settings (e.g., where to log,whether to track agent actions, how large a cache for agent activitytracking, where to look for tools.jar, whether to launch the agentprofiler as a separate thread versus in the JVM premain calling thread,and so on), a number of other configurations may be set for controlledoperation of the agent profiler:

-   -   Core Instrumentation Configurations:        -   Enable/Disable the agent profiler transformer (enabled to            allow any agent to transform and retransform);        -   Where to write modified classes;        -   Where to write original classes;        -   Whether to log modified and original classes;        -   Whether to detect and comment on conflicts;        -   Whether and how to stop conflicts;        -   Whether to verify class correctness (if fails—class is not            modified);        -   Whether to compare classes and outline differences;        -   How many classes at a time to allow in a retransform; and        -   Whether to log all retransformed classes.    -   Class Filtering Configurations:        -   Whether an instrumentation filter applies for all actions            (whether to apply to either just retransforms OR to            getAllLoadedClasses, retransform, transform, and modify            class);        -   Which classes to include (only allow these classes to be            seen); and        -   Which classes to exclude (exclude these classes from being            seen).    -   Global Agent Enablement Configurations:        -   Enable/Disable agents to getAllLoadedClasses;        -   Enable/Disable redefining classes;        -   Enable/Disable transform of classes;        -   Enables/Disable classes to be modified in transform; and        -   Enable/Disable retransform of classes.    -   Additional JVM Tracking Configurations:        -   Whether to track lock contention;        -   Whether to track Garbage Collection; and        -   Whether to track thread CPU.    -   Diagnostics Server Configurations:        -   Which port for the server;        -   Which URL/context for the server (e.g., /agentprofiler); and        -   Password for server.    -   Stack Sampling Configurations:        -   Consider particular threads idle if executing any listed            methods        -   Consider particular threads idle if certain strings are in            the current stack;        -   Only sample threads that contains a particular string;        -   Only sample stacks if thread stack contains this            class/method/etc. string;        -   Whether to include certain threads;        -   Whether to include certain stacks;        -   Maximum number of threads to save in sampler; and        -   Maximum number of Hotspot methods to save in sampler.    -   Specific Software Agent Configurations:        -   The name of the agent jar file with the premain entry;        -   The fully qualified directory path to the agent jar file;        -   A unique managed agent name to be referred to;        -   Additional javaagent parameters to be passed;        -   Whether or not to load the software agent;        -   Additional java agent properties;        -   Additional jars to add to the boot classpath;        -   Additional packages that belong to this agent;        -   Whether or not the agent is active;        -   Whether to allow all loaded classes to be sent to this            agent;        -   Whether to allow for the use of redefineClasses API by this            agent;        -   Whether to allow transforms by this agent;        -   Whether to allow modifying classes (allows transforms, but            NOT class byte changes) by this agent; and        -   Whether to allow class retransforms to be generated by this            agent.

According to the present disclosure, thread statistics may be determinedthrough stack sampling and then shown/processed, accordingly. Forinstance, certain threads may be shown (e.g., all monitored, only thosethreads that are active, only those with active methods, etc.), certaininformation/statistics/metrics may be shown, certain rankings may beperformed (e.g., most actions, longest waits, highest CPU times, etc.),and other information, such as determining “hotspots” of methodactivity, and so on, may be determined and shown as configured. Forexample, FIG. 14 illustrates an example table 1400 of sampled stackstatistics, where threads 1410 (with IDs 1420) are shown with theircurrent status 1430, as well as various metrics such as how many sampleswere taken (1440), how many times the thread was active (1450), the CPUusage (max) 1460, wait time 1470 (e.g., cumulative, maximum, average,etc.), and the last trace collected with that thread (1480). Conversely,FIG. 15 illustrates a table 1500 arranged to show sampler “hotspots”,showing an active methods summary (location 1510, samples 1520, activity1530, CPU utilization 1540, last thread 1550, last trace 1560, etc.).

According to one or more embodiments of the techniques herein, the AgentProfiler is a diagnostic tool, providing a test harness (Proxy/Containerenvironment) for launching and monitoring one or more software agents(Java Agents). The Agent Profiler herein is not meant to be areplacement for the software agent(s), but since all instrumentationcalls must pass through the Agent Profiler, the techniques hereinprovide a very granular view of the software agents, and also providethe ability to perform various actions on the software agents withoutchanging code in the agent(s).

In particular, the Agent Profiler provides abilities that not only helpin customer support cases, but also in optimization/refactoring ofdeveloper's agents, through the following capabilities:

-   -   Controlling/Monitoring all activities of the software agent (any        version, and any agent) without code changes or patches to the        software agent itself which is useful in the field to reproduce        crashes (e.g., through stress testing/repetitive        uninstrument/instrument and retransform/transform). The level of        control over the software agent allows the Profiler to force the        agent to perform things that may otherwise be impossible to        replicate in real life. (Note that in one embodiment, Curl        commands may be used to automate the calls for testing, such as        via a curlit.sh script (e.g., running a routine repeated with        optional delays between repetitions) which appends the output to        a curlResults.html file.    -   Monitoring “hyperactivity” in the software agent itself for        diagnostics purposes (e.g., discover the overhead for due to an        agent constantly calling a method, such as getAllLoadedClasses).    -   Monitoring the CPU and Wall Clock time for all agent operations        such as transform, retransform (bulk and individual),        getAllLoadedClasses( )—this is the time the agent takes to do        these functions—for example, there are instances where the agent        is taking long periods of time to re-evaluate classes during        retransform.    -   Mitigating risk between agents in situations where agents had to        run alongside another agent or identify any conflicts. For        example, the Agent Profiler can identify situations where two        agents are instrumenting the same class, and can prioritize one        agent over the other, or else may perform other mitigation        techniques.    -   Recording and comparing class changes (via instrumentation) on        the fly written to a report (e.g., original and modified classes        written to disk in .class format), checking new method sizes,        constant pool attributes, and performing overall class        verification automatically. Also, the classes may be decompiled        into bytecode using local javap utility (if tools.jar is        available).    -   Locating and monitoring agent threads for CPU, Memory, and Lock        Contention overhead.    -   Exposing/flagging high-latency interceptors and overall        interceptor latency time for software agents.    -   Including a “smart Thread Sampler” built in that can useful to        exonerate OR implicate a particular software agent as having an        issue. In addition, the stack sampler may be used to make        recommendations on what should be instrumented by identifying        high latency method calls and code hotspots.

The techniques herein also provide for additional actions after themonitoring is performed. That is, steps can be made to correct anyactions, or to suggest changes to applications/network operations tomanage the application based on the information obtained, such as thefollowing example scenarios:

-   -   A Lock Contention exposes blocked chains (lock holders)        dependencies and can identify to customers clear issues in        architecture.    -   The Stack Sampler has identified hotspots in customer code both        in specific Threads and in class Methods—the Sampler is        configured intelligently to only monitor specific agent and        application threads that have transactions, as well as to detect        when a thread is idle in a thread pool (parked) OR actually        doing something. It reports and highlights all of this (e.g.,        via a web interface).    -   Network contention and issues are identified when “accept”        threads are BLOCKED by another accept, and they have been        blocked for a long time. In this instance, it is clear the        network traffic is overwhelming the software, and remediation        (reporting, mitigation, etc.) needs to take place.    -   The Agent Profiler identifies which classes/methods to allow        instrumentation to optimize a particular software agent on the        fly, and without the need for agent configuration changes.

In closing, FIG. 16 illustrates an example simplified procedure foragent profiler to monitor activities and performance of software agentsin accordance with one or more embodiments described herein. Forexample, a non-generic, specifically configured device (e.g., device200) may perform procedure 1600 by executing stored instructions (e.g.,process 248, such as an application agent profiler process or “AgentProfiler” process). The procedure 1600 may start at step 1605, andcontinues to step 1610, where, as described in greater detail above, asoftware agent profiler process (agent profiler 630)attaches/initializes to an application 610 and a primary instrumentationinterface 640 for the application (e.g., by performing a javaagentswitch operation, as described above). In step 1615, the software agentprofiler process may then discover one or more software agents (e.g.,Java agents) 620 associated with the application (e.g., by reading aconfiguration file of the application), and may then launch the one ormore software agents (e.g., after determining whether to launchparticular software agents—enabling/disabling certain JVMinstrumentation) within an encapsulated container environment of thesoftware agent profiler process. In particular, as described in detailabove, the encapsulated container environment is established byconfiguring each of the one or more software agents, respectively, topoint to a proxy instrumentation interface 642 of the software agentprofiler process 630 instead of the primary instrumentation interface640 for the application.

In step 1625, the software agent profiler process may now receive callsfrom the one or more software agents on the proxy instrumentationinterface of the software agent profiler process, thus allowingtracing/recording/instrumenting software agent activities such as:general calls to the JVM instrumentation API, calls to retransformclasses, changes made to classes in the transform, getAllLoadedClassescalls, and so on.

In step 1630, the techniques herein may then provide for the softwareagent profiler process to “manage” the calls from the one or moreapplication agents prior to the calls being passed to the primaryinstrumentation interface for the application. In particular, thismanaging step may comprise a number of different embodiments asdescribed above. For instance, managing may comprise monitoring resourceusage based on receiving the calls (e.g., CPU, memory allocation, LockWaits/Block Count/Time, etc.), and/or auditing the one or more softwareagents based on the calls (e.g., CPU Time tracking, Lock Contentiontracking, Garbage collection and duration, Thread Count/Creation rate,and so on), and/or also recording and reporting class changes madewithin the calls. Alternatively or in addition, managing in step 1630may comprise determining first whether to pass the call to the primaryinstrumentation interface for the application or to block the call frompassing to the primary instrumentation interface for the application.Further components of managing the calls may be based on mitigatingdetected issues, such as, e.g., monitoring the one or more softwareagents for hyperactivity based on the calls and mitigating detectedhyperactivity, monitoring the one or more software agents for latencytimes based on the calls and mitigating detected high latency,mitigating conflicts between a plurality of software agents of the oneor more software agents, and so on. Step 1630, therefore, essentiallyencompasses many of the agent profiler capabilities described hereinbased on having access to, and control of, the instrumentation interfacebetween the software agents and the application/JVM.

The simplified procedure 1600 may then end in step 1635, notably withthe ability to continue ingesting and clustering data. Other steps mayalso be included generally within procedure 1600. For example, suchsteps (or, more generally, such additions to steps already specificallyillustrated above), may include: generating test conditions usingartificial calls to the primary instrumentation interface for theapplication; performing a stack sampler operation on threads of the oneor more software agents, wherein the stack sampler operation is filteredto monitor only specific threads that are active transaction-basedthreads (e.g., for hotspots, etc.); and so on.

It should be noted that while certain steps within procedure 1600 may beoptional as described above, the steps shown in FIG. 16 are merelyexamples for illustration, and certain other steps may be included orexcluded as desired. Further, while a particular order of the steps isshown, this ordering is merely illustrative, and any suitablearrangement of the steps may be utilized without departing from thescope of the embodiments herein.

The techniques described herein, therefore, provide for an agentprofiler to monitor activities and performance of software agents. Inparticular, the techniques herein can be used to optimize software agentoperation and interaction where the techniques herein specificallycreate an environment that allows for the profiling the agents (e.g.,Java Agents). For instance, the techniques herein provide greaterprecision in terms of identifying where in an application agentinstrumentation should be focused, and may be used as a preproof-of-concept tool, to streamline and make more precise theeffectiveness of monitoring agents with a particular customerapplication by allowing the Agent Profiler to create a customizedconfiguration for the monitoring agent being implemented. Furthermore,the techniques herein may be used to evaluate the performance andcompatibility of particular software agents with other agents operatingon the same application. Notably, the techniques herein can alsoevaluate the performance without requiring load tools to reproducecrashes by putting different stress conditions on the application'sagent(s). Moreover, the techniques herein greatly improve customersupport capabilities, but expanding the visibility and control into thesoftware agents of a particular application.

Specifically, the techniques herein find threads, packages, etc. byusing the agent manifest and essentially mapping package names intoclasses, class loaders, and thread context class loaders, which isunique for discovering the “composition” of the agent. Also the abilityto “chain” the lock holders and lock waiters and tie them back to theagent is unique, and no tool exists that can turn on/off agentfunctions, control un-instrument/instrument, determine conflicts withother agents, and so on, all without making code changes to the agentsthemselves. Last, the stack sampling techniques provide the ability tofocus on agent method/interceptor presence as executing methods,determining what is actively executing, blocked/waiting in execution, orjust “parked” as a thread pool idle thread. (Where, notably, emphasis ofthe sampling is on threads that indicate an active transaction.)

In still further embodiments of the techniques herein, a business impactof the software agents' performance can also be quantified. That is,because of issues related to specific applications/processes (e.g., losttraffic, slower servers, overloaded network links, etc.), variouscorresponding business transactions may have been correspondinglyaffected for those applications/processes (e.g., online purchases weredelayed, page visits were halted before fully loading, user satisfactionor dwell time decreased, etc.), while other processes (e.g., on othernetwork segments or at other times) remain unaffected. The techniquesherein, therefore, can correlate the software agents' performance withvarious business transactions in order to better understand the effecton the business transactions, accordingly.

Illustratively, the techniques described herein may be performed byhardware, software, and/or firmware, such as in accordance with theillustrative agent profiler process 248, which may include computerexecutable instructions executed by the processor 220 to performfunctions relating to the techniques described herein, e.g., inconjunction with corresponding processes of other devices in thecomputer network as described herein (e.g., on network agents,controllers, computing devices, servers, etc.).

According to the embodiments herein, a method herein may comprise:attaching, by a software agent profiler process, to an application and aprimary instrumentation interface for the application; discovering, bythe software agent profiler process, one or more software agentsassociated with the application; launching, by the software agentprofiler process, the one or more software agents within an encapsulatedcontainer environment of the software agent profiler process byconfiguring each of the one or more software agents, respectively, topoint to a proxy instrumentation interface of the software agentprofiler process instead of the primary instrumentation interface forthe application; receiving, by the software agent profiler process,calls from the one or more software agents on the proxy instrumentationinterface of the software agent profiler process; and managing, by thesoftware agent profiler process, the calls from the one or moreapplication agents prior to the calls being passed to the primaryinstrumentation interface for the application.

In one embodiment, attaching comprises: performing a javaagent switchoperation. In one embodiment, discovering comprises: reading aconfiguration file of the application. In one embodiment, the one ormore software agents comprise Java agents. In one embodiment, managingcomprises: determining whether to pass the call to the primaryinstrumentation interface for the application or to block the call frompassing to the primary instrumentation interface for the application. Inone embodiment, managing comprises: auditing the one or more softwareagents based on the calls. In one embodiment, managing comprises:mitigating conflicts between a plurality of software agents of the oneor more software agents. In one embodiment, the method furthercomprises: monitoring resource usage based on receiving the calls. Inone embodiment, the method further comprises: determining whether tolaunch particular software agents of the one or more software agents. Inone embodiment, the method further comprises: generating test conditionsusing artificial calls to the primary instrumentation interface for theapplication. In one embodiment, the method further comprises: monitoringthe one or more software agents for hyperactivity based on the calls;and mitigating detected hyperactivity. In one embodiment, the methodfurther comprises: recording and reporting class changes made within thecalls. In one embodiment, the method further comprises: monitoring theone or more software agents for latency times based on the calls; andmitigating detected high latency. In one embodiment, the method furthercomprises: performing a stack sampler operation on threads of the one ormore software agents, wherein the stack sampler operation is filtered tomonitor only specific threads that are active transaction-based threads.

According to the embodiments herein, a tangible, non-transitory,computer-readable medium herein may have computer-executableinstructions stored thereon that, when executed by a processor on acomputer, may cause the computer to perform a method comprising:attaching a software agent profiler process to an application and aprimary instrumentation interface for the application; discovering oneor more software agents associated with the application; launching theone or more software agents within an encapsulated container environmentof the software agent profiler process by configuring each of the one ormore software agents, respectively, to point to a proxy instrumentationinterface of the software agent profiler process instead of the primaryinstrumentation interface for the application; receiving calls from theone or more software agents on the proxy instrumentation interface ofthe software agent profiler process; and managing the calls from the oneor more application agents prior to the calls being passed to theprimary instrumentation interface for the application.

Further, according to the embodiments herein an apparatus herein maycomprise: one or more network interfaces to communicate with a network;a processor coupled to the network interfaces and configured to executeone or more processes; and a memory configured to store a processexecutable by the processor, the process, when executed, configured to:attach a software agent profiler process to an application and a primaryinstrumentation interface for the application; discover one or moresoftware agents associated with the application; launch the one or moresoftware agents within an encapsulated container environment of thesoftware agent profiler process by configuring each of the one or moresoftware agents, respectively, to point to a proxy instrumentationinterface of the software agent profiler process instead of the primaryinstrumentation interface for the application; receive calls from theone or more software agents on the proxy instrumentation interface ofthe software agent profiler process; and manage the calls from the oneor more application agents prior to the calls being passed to theprimary instrumentation interface for the application.

While there have been shown and described illustrative embodimentsabove, it is to be understood that various other adaptations andmodifications may be made within the scope of the embodiments herein.For example, while certain embodiments are described herein with respectto certain types of networks in particular, the techniques are notlimited as such and may be used with any computer network, generally, inother embodiments. Moreover, while specific technologies, protocols, andassociated devices have been shown, such as Java, TCP, IP, and so on,other suitable technologies, protocols, and associated devices may beused in accordance with the techniques described above. In addition,while certain devices are shown, and with certain functionality beingperformed on certain devices, other suitable devices and processlocations may be used, accordingly. That is, the embodiments have beenshown and described herein with relation to specific networkconfigurations (orientations, topologies, protocols, terminology,processing locations, etc.). However, the embodiments in their broadersense are not as limited, and may, in fact, be used with other types ofnetworks, protocols, and configurations.

Moreover, while the present disclosure contains many other specifics,these should not be construed as limitations on the scope of anyembodiment or of what may be claimed, but rather as descriptions offeatures that may be specific to particular embodiments of particularembodiments. Certain features that are described in this document in thecontext of separate embodiments can also be implemented in combinationin a single embodiment. Conversely, various features that are describedin the context of a single embodiment can also be implemented inmultiple embodiments separately or in any suitable sub-combination.Further, although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

For instance, while certain aspects of the present disclosure aredescribed in terms of being performed “by a server” or “by acontroller”, those skilled in the art will appreciate that agents of theapplication intelligence platform (e.g., application agents, networkagents, language agents, etc.) may be considered to be extensions of theserver (or controller) operation, and as such, any process stepperformed “by a server” need not be limited to local processing on aspecific server device, unless otherwise specifically noted as such.Furthermore, while certain aspects are described as being performed “byan agent” or by particular types of agents (e.g., application agents,network agents, etc.), the techniques may be generally applied to anysuitable software/hardware configuration (libraries, modules, etc.) aspart of an apparatus or otherwise.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. Moreover, the separation of various system components in theembodiments described in the present disclosure should not be understoodas requiring such separation in all embodiments.

The foregoing description has been directed to specific embodiments. Itwill be apparent, however, that other variations and modifications maybe made to the described embodiments, with the attainment of some or allof their advantages. For instance, it is expressly contemplated that thecomponents and/or elements described herein can be implemented assoftware being stored on a tangible (non-transitory) computer-readablemedium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructionsexecuting on a computer, hardware, firmware, or a combination thereof.Accordingly, this description is to be taken only by way of example andnot to otherwise limit the scope of the embodiments herein. Therefore,it is the object of the appended claims to cover all such variations andmodifications as come within the true intent and scope of theembodiments herein.

What is claimed is:
 1. A method, comprising: launching, by a softwareagent profiler process, one or more software agents within anencapsulated container environment of the software agent profilerprocess by configuring each of the one or more software agents,respectively, to point to a proxy instrumentation interface of thesoftware agent profiler process instead of a primary instrumentationinterface for an application that the software agent profiler process isattached to; receiving, by the software agent profiler process, callsfrom the one or more software agents on the proxy instrumentationinterface of the software agent profiler process; and managing, by thesoftware agent profiler process, the calls from the one or more softwareagents prior to the calls being passed to the primary instrumentationinterface for the application.
 2. The method as in claim 1, wherein thesoftware agent profiler process is attached to the application byperforming a javaagent switch operation.
 3. The method as in claim 1,wherein the one or more software agents are discovered by reading aconfiguration file of the application.
 4. The method as in claim 1,wherein the one or more software agents comprise Java agents.
 5. Themethod as in claim 1, wherein managing comprises: determining whether topass a particular call of the calls to the primary instrumentationinterface for the application or to block the particular call frompassing to the primary instrumentation interface for the application. 6.The method as in claim 1, wherein managing comprises: auditing the oneor more software agents based on the calls.
 7. The method as in claim 1,wherein managing comprises: mitigating conflicts between a plurality ofsoftware agents of the one or more software agents.
 8. The method as inclaim 1, further comprising: monitoring resource usage based onreceiving the calls.
 9. The method as in claim 1, further comprising:determining whether to launch particular software agents of the one ormore software agents.
 10. The method as in claim 1, further comprising:generating test conditions using artificial calls to the primaryinstrumentation interface for the application.
 11. The method as inclaim 1, further comprising: monitoring the one or more software agentsfor hyperactivity based on the calls; and mitigating detectedhyperactivity.
 12. The method as in claim 1, further comprising:recording and reporting class changes made within the calls.
 13. Themethod as in claim 1, further comprising: monitoring the one or moresoftware agents for latency times based on the calls; and mitigatingdetected high latency.
 14. The method as in claim 1, further comprising:performing a stack sampler operation on threads of the one or moresoftware agents, wherein the stack sampler operation is filtered tomonitor only specific threads that are active transaction-based threads.15. A tangible, non-transitory, computer-readable medium havingcomputer-executable instructions stored thereon that, when executed by aprocessor on a computer, cause the computer to perform a methodcomprising: launching one or more software agents within an encapsulatedcontainer environment of a software agent profiler process byconfiguring each of the one or more software agents, respectively, topoint to a proxy instrumentation interface of the software agentprofiler process instead of a primary instrumentation interface for anapplication that the software agent profiler process is attached to;receiving calls from the one or more software agents on the proxyinstrumentation interface of the software agent profiler process; andmanaging the calls from the one or more application agents prior to thecalls being passed to the primary instrumentation interface for theapplication.
 16. The computer-readable medium as in claim 15, whereinthe method, for managing, further comprises: determining whether to passthe call to the primary instrumentation interface for the application orto block the call from passing to the primary instrumentation interfacefor the application.
 17. The computer-readable medium as in claim 15,wherein the method, for managing, further comprises: mitigatingconflicts between a plurality of software agents of the one or moresoftware agents.
 18. The computer-readable medium as in claim 15,wherein the method further comprises: generating test conditions usingartificial calls to the primary instrumentation interface for theapplication.
 19. The computer-readable medium as in claim 15, whereinthe method further comprises: performing a stack sampler operation onthreads of the one or more software agents, wherein the stack sampleroperation is filtered to monitor only specific threads that are activetransaction-based threads.
 20. An apparatus, comprising: one or morenetwork interfaces to communicate with a network; a processor coupled tothe network interfaces and configured to execute one or more processes;and a memory configured to store a process executable by the processor,the process, when executed, configured to: launch one or more softwareagents within an encapsulated container environment of a software agentprofiler process by configuring each of the one or more software agents,respectively, to point to a proxy instrumentation interface of thesoftware agent profiler process instead of a primary instrumentationinterface for an application that the software agent profiler process isattached to; receive calls from the one or more software agents on theproxy instrumentation interface of the software agent profiler process;and manage the calls from the one or more application agents prior tothe calls being passed to the primary instrumentation interface for theapplication.